Type 2 diabetes mellitus (T2D) is characterised by insulin resistance which may be combined with relatively reduced insulin secretion. In the early stage of T2D, the predominant abnormality is reduced insulin sensitivity. The risk of developing T2D is largely due to lifestyle factors most notably lack of physical activity and poor diet.
Insulin resistance is characterised by an abnormal regulation of blood glucose concentration leading to excess amounts of glucose circulating the body and a failure of biochemical reactions at the skeletal muscle to utilise the energy source. The role of insulin in stimulating the transport of glucose across the muscle cell membrane via activation of the glucose transporter 4 (GLUT4) is crucial for allowing glucose uptake, however, in diabetic patients, failure to produce insulin in the pancreas leads to diminished amounts of insulin transported into the blood stream (Turcotte & Fisher, 2008). As skeletal muscle is the most responsive to insulin and the main source of blood glucose clearing, it is essential that skeletal muscle is stimulated in the most efficient way to ‘dispose’ of the excess glucose in the blood by physical exercise.
The onset of exercise increases glucose transport at working muscles by stimulating GLUT4 from within the muscle cell to the surface of the cell which causes a number of metabolic changes, the most important being increased glucose uptake (Lund et al., 1995). During the onset of exercise, intra-muscular, readily available stores of energy (glycogen) are utilised as the primary source for muscle contraction, this leads to the recruitment of the Krebs cycle to, in basic terms, recycle energy by additional glucose and sustained oxygen consumption (aerobic glycolysis), however, anaerobic exercise relies heavily on increased stores on muscle glycogen as a result of training and replenishes stores post exercise as stimulation of muscle glucose uptake persists for an extended period of time post-exercise. Glycogen repletion is characterised by a marked and persistent increase in insulin action (Richter, 1996). Based on this premise, it is important to understand which exercise parameters (intensity, duration, frequency and mode) and the characteristics of the individual (presence of disease, fitness and genetic pre-dispositions) are most beneficial to maximising adaptations to the cells involved with muscle glucose clearing (Sigal et al., 2004), especially in diabetic patients.
There is evidence to suggest that endurance and resistance exercise training lead to adaptations specific to that training regime. Endurance exercise training allows skeletal muscle to utilise O2 and blood-borne fuels, whereas resistance training leads to improvements in force generation (muscle hypertrophy and contractile properties). Both training approaches lead to increased muscle GLUT4 which probably contributes to the increased capacity for insulin-stimulated glucose transport in trained subjects which has implications for insulin-resistant patients (Sigal et al., 2004). Furthermore, both training mechanisms are similar with relation to increased glucose disposal, however, resistance training has the advantage by increasing muscle mass (and therefore glucose storage space) (Holten et al., 2004; Ivy, 2002), but also increase mitochondrial function and density which has been found in elderly subjects or potentially diabetes sufferers (Jubrais et al., 2001). Emerging data on the outcome of different resistance training protocols can conflict, a single session reduced glucose infusion rate during an insulin clamp, however no decrease or increase occurred when 3 sessions were performed (Howlett et al., 2007). Conversely, 1 session (3 sets, 8-12 reps during 8 exercises) decreased the glucose area under the curve during an oral glucose tolerance test by ˜12% compared to pre-exercise levels, 24 hours after exercise in T2D women (Fenicchia et al., 2004).
Other studies have shown a 15% increase compared to control group following 3 sets×10 rep.max in upper and lower body exercise during oral glucose tolerance test (6 hours post exercise (Venables et al., 2007). Another resistance training protocol yielded similar results (13% higher glucose absorption) following 8×10 reps at 75% of 1 rep.max after an insulin injection given 24 hours post exercise (Koopman et al., 2005). Results from a study by Babraj et al, (2009) found that following a 2 week high intensity training program of cycle sprint training against 7.5% body weight reduced the area under the plasma glucose by 12%. A similar protocol to Babraj found that sprint interval training (against 7.5% body weight) increased muscle glycogen content by ˜50% which suggests this form of exercise is capable of inducing post-exercise glucose absorption in diabetic patients.
The American College of Sports Medicine (ACSM) recommends a resistance training regime for T2D individuals whenever possible including 8-10 exercises involving major muscle groups with a minimum of 1 set of 10-15 reps to near fatigue. This regime can be altered to increase the number of sets or the intensity of exercise in certain individuals, this data was published prior to studies by Dunstand et al (2002) and Castaneda et al (2002) who found significant results following 3 sets of 8-10 repetitions of >85% 1 rep.max which should be advocated into further studies. Although 1 set may increase muscular strength, it appears that three or more sets of resistance training may produce greater metabolic benefit in type 2 diabetic patients (Sigal et al., 2004).
An area for consideration with regards to resistance training is the type of muscular action performed as evidence has shown that eccentric muscle contractions (muscle lengthening, e.g. elbow extension against a resistance) may actually damage the muscle and inhibit a metabolic adaptation. For instance, 30 mins of down-hill running caused a 36% decline in insulin-stimulated glucose disposal 48 hours after exercise (Kirwan et al., 1992). Likewise 2 days following intense one-legged eccentric exercise (4 sets knee extension/flexion, 5 mins per set using an isokinetic dynamometer) resulted in a decline in muscle GLUT 4 content and 15% decrease in glucose infusion rate during an insulin clamp (Asp et al., 1996).
Implications for high resistance exercise using weights may be acceptable for young individuals or those with longstanding diabetes. Moderate weight training programs that utilize light weights and high repetitions can be used for maintaining or enhancing upper body strength in nearly all patients with diabetes (Turcotte & Fisher, 2008). Current recommendations for improving glycemic control involve performing moderate to vigorous intensity aerobic and resistance exercise for several hours per week (American Diabetes Association, 2008; Lakka et al., 2007). However, the general population fails to follow such regimes due to lack of time, motivation and adherence (Godin et al., 1994), therefore resistance training in a manageable form (such as exergaming) may provide beneficial adaptations which are more appealing to wider public population.
As discussed above, it is accepted that exercise reduces the risk of developing T2D. Indeed, the promotion of exercise is a cost-effective strategy of reducing the risk of people developing T2D in a population. It is also recognised that achieving a healthy balance between energy input and energy expenditure is an important factor in reducing the risk of developing T2D. There has been much work on developing methods of measuring energy expenditure which have been important in helping the understanding of the relationship between physical activity and health. It is recognised that regular and accurate self-monitoring of energy expenditure in the free-living environment can provide important feedback to a patient, thereby increasing self-awareness which is the prerequisite for healthy decision making and long-term lifestyle change.
The, location and type of muscle loading and intensity and duration are all important parameters in the prevention of T2D. At present, there are several known ways of measuring energy expenditure but these are generally focussed on the calorific energy intensity (termed EE) of the whole body. While such data is vital in terms of measuring calories burned, levels of exercise intensity and duration during free-living, the prior art systems are not configured to give specific energy-related information corresponding to the movement of specific, individual parts of the body. Simply taking a systemic measure of overall calories used will generally not be sufficient to quantify the potential benefits of exercise, and will therefore not be sufficient to maximise the potential benefits of future exercise interventions.
The number of calories a person burns is an important and actionable parameter for many applications and disease conditions. These include metabolic disorders, weight control (loss, gain, or maintenance), sports performance, and body composition changes. True total energy expenditure (TEE) is a much more useful parameter but is very difficult to measure, and all known techniques make use of approximations of one kind or another, and/or are impractical due to the nature of data collection. An overview of the techniques for measuring energy expenditure can be found in Andre at al., 2007. Known techniques include indirect calorimetry, the use of doubly labelled water, or the use of heart rate monitors, pedometers, global positioning system (GPS) monitors, accelerometers, multisensor devices or multilocation devices. Each of these prior art systems and methods are described below.
Indirect Calorimetry
Indirect calorimetry measures the oxygen and carbon dioxide that a person inhales and exhales and indirectly determines the calories burned during a given period. This method is undertaken in laboratory conditions using a metabolic cart and is widely regarded in the research community as a standard measurement method, presently. However, most metabolic carts for indirect calorimetry measurements are large and bulky and are not suited for monitoring outside the laboratory setting. In addition, the required devices are expensive, costing in the region of $20,000 for a basic system, although more portable, less costly metabolic carts have now become available. Typically, however, portable systems have higher error rates compared with their larger stationary counterparts. Both stationary and portable metabolic carts require the user to breathe through a mouthpiece or mask and are usually used in a laboratory.
Doubly Labelled Water (DLW)
The DLW stable isotope method is based on the principle that in a loading dose of 2H218O given to a subject, 18O is eliminated from the body as CO2 and water, while deuterium is eliminated from the body as water. The rate of CO2 production, and, thus, energy expenditure, is calculated from the difference of the two elimination rates. The subjects give urine and saliva specimens before and after drinking an initial dose of DLW and then give a final urine specimen 1 to 2 weeks later. During the period between initial and final samplings, subjects are free to carry out their normal activities. This is a safe procedure, as the isotopes are stable and emit no radiation. Limitations of the DLW method include a high cost (˜$1500/person), the need for specialized equipment and expertise to implement the techniques. Additionally, the method can only be used to measure expenditure over a long period of time (e.g., 10-14 days). DLW has an error rate of about 5% over a 2-week period because of starting and ending conditions.
Heart Rate Monitors
Heart rate (HR) is one of the fundamental vital signs and is related to the level of physical exertion. A person's HR increases linearly with oxygen consumption, especially for moderate to strenuous activity. HR monitoring is quite common and is often used as part of an exercise prescription. Furthermore, most HR monitor companies have released software for converting HR data into an estimate of energy expenditure (e.g., Polar, Kempele, Finland). Several studies have found that calibration is required to create a curve between the subject's HR and estimated energy expenditure, involving a submaximal stress test at moderate activity levels. Additionally, HR monitors are typically only accurate for moderate to vigorous activities, as in lower-intensity activities. Confounds such as stress, emotions, caffeine intake, ambient temperature, or illness may have a significant impact on a person's HR and may therefore skew results.
Chest-strap HR monitors can be a burden to participants because of the constriction required across the chest to maintain good skin contact. Electrode-based HR monitors are difficult to wear, as placement, skin treatment, and irritation can be significant issues and detriments to long-term wear. Subjects have shown poor compliance at wearing heart rate monitors in free-living trials. Additionally, many HR monitors receive interference from electrical equipment. Thus, signal transmission is prone to interference.
Pedometers
Pedometers, by definition, measure footfalls. The advantage of pedometers is their low cost, ranging from $15 to $300, and wide availability. In general, pedometers are not accurate when used for activities that do not involve footfalls (e.g., weight lifting, biking, household activities). Even for ambulatory activities, pedometers have been found to be inaccurate at both counting steps and assessing distance walked.
In most cases, pedometers (at the higher end) can be accurate at counting steps, although they are much less accurate at predicting energy expenditure, even during walking, with error rates of ±30%. Whilst a pedometer can be used as a coaching or self-monitoring tool to help people set goals and increase their physical activity levels, they do not measure the intensity, duration, or frequency of physical activity.
Global Positioning System (GPS) Monitors
Several devices based on GPS are known that compute speed and distance traveled and, from that information, estimate calories expended for a particular activity (e.g., walking/running, road biking). The accuracy of these products is only beginning to be assessed adequately. Even for outdoor activity, where the GPS signal is strongest, some research indicates that these products may overestimate energy expenditure except for fast walking. Although GPS receivers have become quite wearable for short durations, long-term wear may be uncomfortable. Furthermore, because the monitors only work outdoors and for activities involving true translational motion, these devices have significant limitations with respect to being a suitable free-living monitor of energy expenditure. Most currently available devices either report their results on the device itself or to a personal computer.
Multisensor Devices
Most of the single-sensor based systems that are appropriate for free-living activities involve surrogates for energy expenditure, e.g. measuring steps, motion, heart rate, location on the planet, or expired oxygen. All of these quantities provide indirect measures of energy expenditure.
Low motion might indicate rest or it might indicate physical activity using a part of the body far from the accelerometer. Moderate motion might indicate physical activity or it might indicate riding in a moving vehicle on a rough road. By adding another variable, such as heart rate, these different contexts can be disambiguated. For example, riding in a car will typically induce lower heart rates than moderate physical activity, and subjects at rest will typically have lower heart rates than those performing low-motion physical activity. By taking advantage of the science of data fusion, multisensor systems typically achieve higher accuracies than single sensor systems while typically keeping overall costs moderate.
Like single sensor devices, multisensor devices require sensors in skin contact which may be inconvenient or impractical for the type of activity being monitored.
Another multisensor system is the Garmin® Forerunner, which utilizes GPS, heart rate, and optional foot pod and biking cadence/speed sensors to provide “fill in” data if the GPS signal drops out.
A further multisensor monitor is the SenseWear® Pro3 (BodyMedia Inc., Pittsburgh, Pa.). The SenseWear® armband (SWA) is a small, wireless, and wearable body monitor worn on the back of the upper right arm. The SWA utilizes a combination of sensors. A proprietary heat-flux sensor measures the amount of heat being dissipated by the body by measuring the heat loss along a thermally conductive path between the skin and a vent on the side of the armband. Skin temperature and near-armband temperature are also measured by sensitive thermistors. The armband also measures galvanic skin response (the conductivity of the wearer's skin), which varies as a consequence of physical and emotional stimuli. A two-axis accelerometer tracks the movement of the upper arm and provides information about body position. Additionally, a wireless display device is available that can be worn as a watch or clipped to clothing that displays the calories burned, steps taken, and minutes spent in moderate and vigorous physical activity for today, yesterday, and from the time a trip button was pressed.
The SWA utilizes pattern detection algorithms that utilize the physiologic signals from all the sensors to first detect the wearer's context and then apply an appropriate formula to estimate energy expenditure from the sensor values. The armband can recognize many basic activities, such as weight lifting, walking, running, biking, resting, and riding in a car, bus, or train. Other activities are classified into combinations of these basic activities; for example, baseball could be broken down into a combination of mostly near-restful activity and running. The armband can be worn comfortably during a person's normal life and does not require any time in the laboratory for uncomfortable measurements. Laboratory tests indicate that the device is accurate across a broad range of activities and performs well when compared to DLW in diabetic and obese subjects with only an 8% average error.
Accelerometers
Accelerometers operate by measuring acceleration along a given axis, using any of a number of technologies, including piezoelectric, micromechanical springs, and changes in capacitance. Often, multiple axis measurements are bundled into a single package, allowing two and three axis accelerometers. The major function of accelerometers is that the sensor converts movements into electrical signals that are proportional to the muscular force producing motion. Most accelerometers compute energy expenditure by first rectifying the accelerometer signal and then integrating to compute accelerometer counts. Typically, these counts are then multiplied by a constant and added to a separate constant to compute energy expenditure.
Moreover, accelerometer equations have been developed for specific activities (e.g., walking and running, sometimes rest) and do not estimate other activities accurately (e.g., stationary biking, elliptical trainer). Additionally, accelerometers are subject to motion artifacts from activities such as driving in a car or riding on a train. The consensus appears to be that for activities composed entirely of flat-ground ambulation and rest, accelerometers can provide objective measures of activity. Advantages of these types of activity monitors are that they are low to moderate in cost ($50 to more than $1000) and are typically relatively easy to use. Because of the complex nature of some of these devices, as well as the size, subject compliance can sometimes become an issue.
More complex equations for estimating energy expenditure from counts are being developed in this technological area. In these methods, the coefficient of variation of the accelerometry signal is utilized to select an appropriate regression equation. This works because the coefficient of variation of regular walking activity is lower than for free-living activities such as house cleaning. Essentially, this idea utilizes two aspects of a signal: first to classify and then to predict.
Additionally, it is known that the indirect process of determining position from accelerometry data (and accelerometer-based systems [e.g. inclinometers]) is problematic. Errors rapidly accumulate during the integration process and additional information (such as initial conditions) are required for determination of integration constants. Consequently, attempts to track motion by integration of even the most accurate accelerometer signals have been unsuccessful unless low-pass filtering is permitted at each integration or very high quality, expensive and bulky equipment is used. For the purposes of exergaming where an unknown range of non-cyclical movements will occur and the gamer must be free to move unrestricted, neither approach is feasible.
Multilocation Devices
Given that some of the problems of predicting energy expenditure from motion come from an activity that utilizes a part of the body not being measured (e.g., stationary biking), one solution is to utilize accelerometers on multiple parts of the body. Two devices, the DynaPort (McRoberts, B V, The Hague, The Netherlands) and the IDEEA monitor (MiniSun, Fresno, Calif.), utilize this technique. The IDEEA monitor classifies more than 30 activities, with high reported accuracy, and utilizes five accelerometers attached via medical tape to the chest, the underside of each foot, and the front of each thigh. Wires connect the accelerometers to a belt-worn recorder. The accuracy of the device appears to be good, as they are reported to be accurate to within 10% for energy expenditure for some activities. In general, these devices tend to be expensive (more than $1000) and have a significant ease-of-use problem. Many single-location devices are seen as unattractive and inconvenient by the user, especially those that require taping multiple electrodes to locations only accessible when the user is disrobed.
An example of a known system for monitoring the exertion of a user is described in U.S. Pat. No. 5,524,637, which comprises one or more sensors attached to a user for measuring the user's motion. An algorithm is used to determine what kind of activity the user is doing, and at what level of intensity, based on the sensor measurements. A level of overall exertion (e.g. number of calories burned) is then estimated from a look-up table, matrix, formula, or decision flowchart based on the determined type and level of exercise.
It is an object of the present invention to provide an alternative method and apparatus for measuring the total expended energy of a moving body that, in at least one embodiment, improves over the prior art.